{"id":2416,"date":"2011-01-09T11:51:30","date_gmt":"2011-01-09T11:51:30","guid":{"rendered":"http:\/\/hgpu.org\/?p=2416"},"modified":"2011-01-09T11:51:30","modified_gmt":"2011-01-09T11:51:30","slug":"parallel-prefix-sum-scan-with-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2416","title":{"rendered":"Parallel Prefix Sum (Scan) with CUDA"},"content":{"rendered":"<p>Parallel prefix sum, also known as parallel Scan, is a useful building block for many parallel algorithms including sorting and building data structures. In this document we introduce Scan and describe step-by-step how it can be implemented efficiently in NVIDIA CUDA.  We start with a basic naive algorithm and proceed through more advanced techniques to obtain best performance.  We then explain how to scan arrays of arbitrary size that cannot be processed with a single block of threads.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Parallel prefix sum, also known as parallel Scan, is a useful building block for many parallel algorithms including sorting and building data structures. In this document we introduce Scan and describe step-by-step how it can be implemented efficiently in NVIDIA CUDA. We start with a basic naive algorithm and proceed through more advanced techniques to [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,20,183,134],"class_list":["post-2416","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-visualization"],"views":5765,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2416","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2416"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2416\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2416"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2416"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2416"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}